similar to: using subset() in data frame

Dear List-members, Hopefully someone will help through my confusion: In order to get the same coefficients as we get from the following ## require (MASS) summary ( lm(Gas ~ Insul/Temp - 1, data = whiteside) ) ...................... we need to do the following (if we use model.matrix to specify the model) ## summary ( lm(Gas ~ model.matrix(~ Insul/Temp - 1) - 1, data = whiteside) )

I am having difficulty understanding how I would constrain a data set by filtering out 'records' based on certain criteria. Using SQL I could query using 'select * from my.data where LithClass in ('sand', 'clay')' or some such. Using subset, there seem to be ghosts left behind (that is, all of the LithClass *.Labels* remain after subset) > dput(tcc)

hello, I am very new to R. My current data set is a mix of values and categories. It is a geoscience data set, with values per rock sample. Case in point, each sample belongs to a lithology class, and each sample has several physical property measurements (density, porosity...). I want to be able to plot these physical properties for all samples in each lithology class. this is how i'm doing

A pragmatic solution could be to create a simple linear regression example with variables in the global environment and then another example with a data.frame. The latter might be somewhat more complex, e.g., with several regressors and/or mixed categorical and numeric covariates to illustrate how regression and analysis of (co-)variance can be combined. I like to use MASS's whiteside

Hi, How would one go about determining if the slope terms from an analysis of covariance model are different from eachother? Based on the example from MASS: library(MASS) # parallel slope model l.para <- lm(Temp ~ Gas + Insul, data=whiteside) # multiple slope model l.mult <- lm(Temp ~ Insul/Gas -1, data=whiteside) # compare nested models: anova(l.para, l.mult) Analysis of Variance

Page 140 of MASS uses the function xyplot. But I don't find it in R. Is there a package that I should load to use xyplot. Or there is a function with a different name in R that does the same thing as xyplot in S. xyplot(Gas ~ Temp | Insul, whiteside, panel = function(x, y, ...) { panel.xyplot(x, y, ...) panel.lmline(x, y, ...) }, xlab = "Average external temperature (deg. C)", ylab

R-helpers; I tried to get a superscripted 3 in the following xyplot example but failed: >data(whiteside) >xyplot(Gas ~ Temp | Insul, whiteside, panel = function(x, y, ...) { panel.xyplot(x, y, ...) panel.lmline(x, y, ...) }, xlab = "Average external temperature (deg. C)", ylab = paste(paste("Gas consumption (1000", expression(ft^3),")"), aspect

On Sat, 15 Dec 2018, frederik at ofb.net wrote: > I agree with Steve and Achim that we should keep some examples with no > data frame. That's Objectively Simpler, whether or not it leads to > clutter in the wrong hands. As Steve points out, we have attach() > which is an excellent language feature - not to mention with(). Just for the record: Personally, I wouldn't recommend

so i am having this question what should i do if the give data file (.txt) has 4 columns, but different lengths? how can i read them in R? any idea for the following problem? Gas consumption (1000 cubic feet) was measured before and after insulation was put into a house. We are interested in looking at the effect of insulation on gas consumption. The average outside temperature (degrees celcius)

gday R gurus, I have a multivariate regression for which I want to constrain the coefficients to be > 0. Is this possible? I've check the doco and searched CRAN but can't find anything. thanks, John Strumila -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send

In "Modern Applied Statistics with S-Plus" 3rd ed., footnote on page 153 regarding a model lm(Gas~Insul/(Temp+I(Temp^2))-1,whiteside), I read "Notice that when the quadratic terms are present, first degree coefficients mean 'the slope of the curve at temperature zero', so a non-significant value does not mean that the linear term is not needed.

Hello everyone, Can anyone tell me how do I restore data in a data.frame provided by base R libraries (MASS) ? I uninstalled R then installed it again and I still see the new changes I made!!!??? (eg. whiteside$Temp=1 ==> 1 overwrote all the rows, I want the old values :( !! Please HELP! -- View this message in context:

I think this site is always failing. It has an embedded video. http://www.sportsnet.ca/basketball/nba/free-association-podcast-much-toronto-raptors-fear-miami-heats-dwayne-wade-hassan-whiteside-joe-johnson/ I tried looking at the output of strace firefox > fferrors.txt 2>&1 grep ENOENT fferrors.txt hope that helps Note: I also have ffmpeg with nux's repo installed On Mon, May

FWIW, before all the examples are changed to data frame variants, I think there's fairly good reason to have at least _one_ example that does _not_ place variables in a data frame. The data argument in lm() is optional. And there is more than one way to manage data in a project. I personally don't much like lots of stray variables lurking about, but if those are the only variables out

Dear list members I have a doubt on how p-values for t-statistics are calculated in the summary of Linear Models. Here goes an example: x <- rnorm(100,50,10) y <- rnorm(100,0,5) fit1<-lm(y~x) summary(fit1) summary(fit1)$coef[2] # b summary(fit1)$coef[4] # Std. Error summary(fit1)$coef[6] # t-statistic summary(fit1)$coef[8] # Pr(>|t| summary(fit1)$df [2] # degrees of freedom #

Hi, There is something I don't get with object of class "mle" returned by a function I wrote. More precisely it's about the behaviour of method "confint" and "profile" applied to these object. I've written a short function (see below) whose arguments are: 1) A univariate sample (arising from a gamma, log-normal or whatever). 2) A character string

Dear R users, Could somebody please help me to find a way of comparing nonlinear, non-nested models in R, where the number of parameters is not necessarily different? Here is a sample (growth rates, y, as a function of internal substrate concentration, x): x <- c(0.52, 1.21, 1.45, 1.64, 1.89, 2.14, 2.47, 3.20, 4.47, 5.31, 6.48) y <- c(0.00, 0.35, 0.41, 0.49, 0.58, 0.61, 0.71, 0.83, 0.98,

Dear R Helpers, I have noticed that when I use lmer to analyse data, the summary function gives different values for the AIC, BIC and log-likelihood compared with the anova function. Here is a sample program #make some data set.seed(1); datx=data.frame(array(runif(720),c(240,3),dimnames=list(NULL,c('x1','x2','y' )))) id=rep(1:120,2); datx=cbind(id,datx) #give x1 a

Hi, I am trying to do logistic regression for data of 104 patients, which have one outcome (yes or no) and 15 variables (9 categorical factors [yes or no] and 6 continuous variables). Number of yes outcome is 25. Twenty-five events and 15 variables mean events per variable is much less than 10. Therefore, I tried to analyze the data with penalized regression method. I would like please some of the

Hi, I am trying to construct a svmpoly model using the "caret" package (please see code below). Using the same data, without changing any setting, I am just changing the seed value. Sometimes it constructs the model successfully, and sometimes I get an ?Error in indexes[[j]] : subscript out of bounds?. For example when I set seed to 357 following code produced result only for 8